SAR Image Simulation by Generative Adversarial Networks

Abstract

SAR image simulation plays an important role in the process of SAR target interpretation and recognition, especially when the number of SAR images is limited. Due to the restriction of acquisition process, the numbers of SAR target images are always insufficient. The traditional SAR image simulation, which is based on calculation of electromagnetic theory, is easily to be affected by parameter distortion due to the lack of joint optimization. Consequently, it makes a big effect on the quality of the simulated images. This paper presents a novel approach, end-to-end models, to simulate the desired images from the SAR image database. A series of generative adversarial networks include DCGAN, weight clipping WGAN and WGAN with gradient penalty are optimized and applied to generate typical SAR target images. Three kinds of network structures are used, include structure of DCGAN, newly proposed structure of four residual blocks networks and Resnet. Experimental results show that the proposed novel method is not only efficient for SAR image simulation, but also can generate excellent SAR images. Furthermore, we analysis the results and the characteristics of different networks, which pave a good way for SAR image simulation based on artificial intelligence method.